Recommendation engine

Personal recommendations for every user, in real time.

vecsai Recommend learns from what each user views, buys, watches and skips, and from what the items themselves are about. Recommendations update as the session unfolds, new items can be recommended on the day they are added, and you decide what may and may not be shown.

vecsai Recommend · home screen

Because you watched The Long Orbit

  • Deep Signal
  • Red Horizon
  • Quiet Station
  • Last Light

New this week for you

  • Northbound
  • Salt & Iron
  • The Archive
  • Glasswater
Updated after every playexample
Features

What vecsai Recommend does

Real-time personalisation

Every view, click and purchase updates the next recommendation. No nightly batch job.

Cold start, solved

New items are recommended from their content before they have any interactions. New users get relevant picks from their first clicks.

Many scenarios, one engine

Items for a user, items similar to an item, users for an item, next to watch, frequently bought together.

Business rules

Filter by stock, region or rights; boost margins or new releases; keep pinned positions fixed.

Diversity and freshness

Control how varied recommendations are and how strongly recent content is favoured.

Tested before release

Run a new recommendation strategy through vecsai Sim before your users ever see it.

Use cases

Where teams use it

Homepage and feeds

A personal homepage or feed for every user, refreshed as they browse.

“You may also like”

Similar and complementary items on product, article and video pages.

Email and push

Personal picks for newsletters and notifications, under the same rules as the site.

FAQ

Questions about vecsai Recommend

What data do you need?

A catalogue of items with their attributes, and the interactions users have with them — views, purchases, plays, ratings. More signal helps, but you can start small.

How quickly do recommendations react?

Interactions are applied in real time, so the next request already reflects what the user just did.

Can we use it alongside our own models?

Yes. Use vecsai as the primary engine, or as a candidate source that your own ranking layer re-orders.

Works with

The rest of the platform

See vecsai on your own catalogue

Tell us about your platform and we will show you search, recommendations and simulation running on your data.